Jiang Xiao, Yu Haibin, Yang Jiayu, Liu Xiaoli, Li Zhu
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.
Neurology Department, Zhejiang Hospital, Zhejiang 310013, China.
Med Eng Phys. 2025 May;139:104333. doi: 10.1016/j.medengphy.2025.104333. Epub 2025 Apr 9.
Parkinson's disease (PD) remains a condition without a cure, though its early manifestations can be managed effectively by medical professionals. This underscores the significance of early detection of PD. It has been widely demonstrated that handwriting analysis is a promising avenue for early PD diagnosis. In recent research, there has been a pivot towards leveraging artificial intelligence (AI) technologies for analyzing handwriting images to aid in diagnosing the disease. This study introduces an innovative network architecture specifically designed to capture the nuances of tremor and irregular spacing characteristic of PD patients' handwriting. By incorporating an attention mechanism, this network is capable of prioritizing different areas within the handwriting feature map, according to their diagnostic relevance. This approach significantly enhances the accuracy of detecting PD through handwriting analysis, with our model achieving an impressive mean accuracy rate of 96.5 %. When compared to traditional convolutional neural networks, our attention-based continuous convolutional network model demonstrates a substantial increase in diagnostic precision.
帕金森病（PD）仍然是一种无法治愈的疾病，不过其早期症状可以由医学专业人员有效控制。这凸显了早期发现帕金森病的重要性。大量研究表明，笔迹分析是早期诊断帕金森病的一个有前景的途径。最近的研究已转向利用人工智能（AI）技术分析笔迹图像以辅助疾病诊断。本研究引入了一种创新的网络架构，专门设计用于捕捉帕金森病患者笔迹中震颤和不规则间距的细微差别。通过纳入注意力机制，该网络能够根据笔迹特征图中不同区域的诊断相关性对其进行优先级排序。这种方法显著提高了通过笔迹分析检测帕金森病的准确性，我们的模型实现了令人印象深刻的96.5%的平均准确率。与传统卷积神经网络相比，我们基于注意力的连续卷积网络模型在诊断精度上有显著提高。
